Cascaded neural network surrogate modeling for real-time decision-making in long-distance water supply distribution

Engineering Applications of Computational Fluid Mechanics, Год журнала: 2025, Номер 19(1)

Опубликована: Янв. 16, 2025

Effective water distribution in long-distance supply systems requires precise control over pump station operations and flow-regulating elements, such as speeds valve openings, typically achieved through hydraulic models. However, traditional models are time-intensive to develop require frequent calibration, limiting their practicality for real-time applications. This paper presents a cascaded neural network (CNN) model that integrates classification regression components serve an efficient surrogate decision-making. In the proposed CNN model, component identifies number of pumps needed meet system flow demands, while predicts target values openings. Considering nonlinear relationship between rate regulating error was introduced evaluation metric via Orthogonal-Triangular (QR) decomposition. The model's performance robustness were validated using data from actual system, including analyses its sensitivity uncertainties reservoir level measurements. Results demonstrate achieves more accurate predictions compared pure networks. Furthermore, uncertainty analysis reveals is less affected by measurement errors, it sensitive underscoring importance monitoring practical

Язык: Английский

Enhancing the accuracy and generalizability of reference evapotranspiration forecasting in California using deep global learning

Journal of Hydrology Regional Studies, Год журнала: 2025, Номер 59, С. 102339 - 102339

Опубликована: Март 26, 2025

Язык: Английский

Long-term prediction of Poyang Lake water level by combining multi-scale isometric convolution network with quantile regression

Journal of Hydrology Regional Studies, Год журнала: 2025, Номер 59, С. 102365 - 102365

Опубликована: Апрель 17, 2025

Язык: Английский